Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis

BACKGROUND

Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers.

METHODS

The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 10-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample.

RESULTS

Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = 0.811; balanced accuracy = 0.745, sensitivity = 0.765; specificity = 0.726). The best predictors of burnout were Beck's Depression Inventory scores, Athen's Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status.

CONCLUSIONS

The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Work-related burnout among public secondary school teachers is significantly influenced by the psychosocial work factors: a cross-sectional study from Ethiopia

Introduction

Work-related burnout (WRB) is the degree of physical and psychological fatigue and exhaustion perceived by individuals as related to their work. Even though the condition is widespread across various occupations, teachers inevitably experience high levels of burnout in their work, which can have long-term effects on their health and well-being. However, in developing countries such as Ethiopia, the lack of reliable data on psychosocial hazards, including work-related burnout, often encumbers officials from planning preventive measures. This study investigated the prevalence and contributing factors of work-related burnout among public secondary school teachers in Gondar City, northwestern Ethiopia.

Methods

An institution-based cross-sectional study was conducted from May to June 2022. A sample of 588 teachers was recruited using simple random sampling. Work-related burnout was assessed using a standardized seven-item Copenhagen Burnout Inventory (CBI). The data were collected through a self-administered questionnaire. The collected data were entered into EpiData and analyzed using SPSS. A multivariable logistic regression analysis was used to identify factors associated with work-related burnout.

Results

The overall response rate was 94.05% (N = 553). The majority of participants, 356 (64.4%), were male. The mean (±SD) age of the participants was 38.74 (±7.65) years. This study demonstrated that the prevalence of work-related burnout among school teachers in the past 12 months was 37.4% (n = 207). High job demands, job stress, job dissatisfaction, low role clarity, and student demotivation were found to be the psychosocial work factors that significantly influenced the prevalence of work-related burnout among school teachers.

Conclusion

This study highlights the high prevalence of work-related burnout among public secondary school teachers. Psychosocial work factors such as job demands, job stress, job satisfaction, role clarity, and student demotivation were significant factors influencing work-related burnout. To reduce the condition, it is recommended to take measures to cope with high job demands, improve stress management skills, promote job satisfaction strategies, clarify teachers' responsibilities, and use effective teaching practices to motivate students. In general, addressing psychosocial work factors needs to be central to efforts to prevent teacher burnout.

Non-technical losses detection in energy consumption focusing on energy recovery and explainability

Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classification algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be easily solved and which compromise the quality and fairness of the predictions. In this work, we contextualise these problems in an NTL detection system built for an international utility company. We explain how we have mitigated them by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding. As we show in this work, the regression approach can be a good option to mitigate these technical problems, and can be adjusted in order to capture the most striking NTL cases. Moreover, explainable AI (through Shapley Values) allows us to both validate the correctness of the regression approach in this context beyond benchmarking, and improve the transparency of our system drastically.